Machine Learning A-Z™: Hands-On Python & R In Data Science

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

All Levels 4.5(124,330 Ratings) 658,707 Students enrolled
Created by Kirill Eremenko Last updated 06/2020 English ["English [Auto-generated]","French [Auto-generated]","German [Auto-generated]","Italian [Auto-generated]","Portuguese [Auto-generated]","Spanish [Auto-generated]"]
What will i learn?
  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make powerful analysis
  • Make robust Machine Learning models
  • Create strong added value to your business
  • Use Machine Learning for personal purpose
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Curriculum for this course
328 Lectures 43:49:24
Welcome to the course!
5 Lectures 00:33:36
  • Applications of Machine Learning 00:03:22
  • BONUS: Learning Paths 00:00:51
  • BONUS #2 ML vs DL vs AI — What’s the Difference? 00:00:13
  • BONUS #3 Regression Types 00:00:12
  • Why Machine Learning is the Future 00:06:37
  • Important notes, tips & tricks for this course 00:02:01
  • This PDF resource will help you a lot! 00:01:04
  • Updates on Udemy Reviews 00:01:09
  • GET ALL THE CODES AND DATASETS HERE! 00:01:07
  • Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder 00:16:48
  • Installing R and R Studio (Mac, Linux & Windows) 00:05:40
  • BONUS: Meet your instructors 00:00:28
  • Some Additional Resources 00:00:10
  • FAQBot! 00:00:59
  • Welcome to Part 1 - Data Preprocessing 00:00:21
  • Make sure you have your Machine Learning A-Z folder ready 00:00:15
  • Getting Started 00:10:50
  • Importing the Libraries 00:03:34
  • Importing the Dataset 00:15:42
  • For Python learners, summary of Object-oriented programming: classes & objects 00:01:00
  • Taking care of Missing Data 00:12:15
  • Encoding Categorical Data 00:14:58
  • Splitting the dataset into the Training set and Test set 00:13:47
  • Feature Scaling 00:20:31
  • Welcome 00:00:24
  • Getting Started 00:01:35
  • Make sure you have your dataset ready 00:00:08
  • Dataset Description 00:01:57
  • Importing the Dataset 00:02:44
  • Taking care of Missing Data 00:06:22
  • Encoding Categorical Data 00:06:02
  • Splitting the dataset into the Training set and Test set 00:09:34
  • Feature Scaling 00:09:14
  • Data Preprocessing Template 00:05:15
  • Welcome to Part 2 - Regression 00:00:22
  • Simple Linear Regression 5 questions
  • Simple Linear Regression Intuition - Step 1 00:05:45
  • Simple Linear Regression Intuition - Step 2 00:03:09
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Simple Linear Regression in Python - Step 1 00:12:48
  • Simple Linear Regression in Python - Step 2 00:07:56
  • Simple Linear Regression in Python - Step 3 00:04:35
  • Simple Linear Regression in Python - Step 4 00:12:56
  • Simple Linear Regression in Python - BONUS 00:00:27
  • Simple Linear Regression in R - Step 1 00:04:40
  • Simple Linear Regression in R - Step 2 00:05:58
  • Simple Linear Regression in R - Step 3 00:03:38
  • Simple Linear Regression in R - Step 4 00:15:55
  • Multiple Linear Regression 5 questions
  • Dataset + Business Problem Description 00:03:44
  • Multiple Linear Regression Intuition - Step 1 00:01:02
  • Multiple Linear Regression Intuition - Step 2 00:01:00
  • Multiple Linear Regression Intuition - Step 3 00:07:21
  • Multiple Linear Regression Intuition - Step 4 00:02:10
  • Understanding the P-Value 00:11:44
  • Multiple Linear Regression Intuition - Step 5 00:15:41
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Multiple Linear Regression in Python - Step 1 00:08:30
  • Multiple Linear Regression in Python - Step 2 00:09:11
  • Multiple Linear Regression in Python - Step 3 00:10:37
  • Multiple Linear Regression in Python - Step 4 00:12:31
  • Multiple Linear Regression in Python - Backward Elimination 00:01:34
  • Multiple Linear Regression in Python - BONUS 00:00:28
  • Multiple Linear Regression in R - Step 1 00:07:50
  • Multiple Linear Regression in R - Step 2 00:10:25
  • Multiple Linear Regression in R - Step 3 00:04:26
  • Multiple Linear Regression in R - Backward Elimination - HOMEWORK ! 00:17:51
  • Multiple Linear Regression in R - Backward Elimination - Homework Solution 00:07:33
  • Multiple Linear Regression in R - Automatic Backward Elimination 00:00:15
  • Polynomial Regression Intuition 00:05:08
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Polynomial Regression in Python - Step 1 00:13:30
  • Polynomial Regression in Python - Step 2 00:11:40
  • Polynomial Regression in Python - Step 3 00:12:54
  • Polynomial Regression in Python - Step 4 00:08:10
  • Polynomial Regression in R - Step 1 00:09:12
  • Polynomial Regression in R - Step 2 00:09:58
  • Polynomial Regression in R - Step 3 00:19:54
  • Polynomial Regression in R - Step 4 00:09:35
  • R Regression Template 00:11:58
  • SVR Intuition (Updated!) 00:08:09
  • Heads-up on non-linear SVR 00:03:57
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • SVR in Python - Step 1 00:09:15
  • SVR in Python - Step 2 00:15:10
  • SVR in Python - Step 3 00:06:27
  • SVR in Python - Step 4 00:08:01
  • SVR in Python - Step 5 00:15:40
  • SVR in R 00:11:44
  • Decision Tree Regression Intuition 00:11:06
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Decision Tree Regression in Python - Step 1 00:08:38
  • Decision Tree Regression in Python - Step 2 00:05:00
  • Decision Tree Regression in Python - Step 3 00:03:16
  • Decision Tree Regression in Python - Step 4 00:09:50
  • Decision Tree Regression in R 00:19:54
  • Random Forest Regression Intuition 00:06:44
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Random Forest Regression in Python 00:13:23
  • Random Forest Regression in R 00:17:42
  • R-Squared Intuition 00:05:11
  • Adjusted R-Squared Intuition 00:09:56
  • Make sure you have this Model Selection folder ready 00:00:31
  • Preparation of the Regression Code Templates 00:19:26
  • THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! 00:09:03
  • Conclusion of Part 2 - Regression 00:01:03
  • Evaluating Regression Models Performance - Homework's Final Part 00:08:54
  • Interpreting Linear Regression Coefficients 00:09:16
  • Conclusion of Part 2 - Regression 00:01:03
  • Welcome to Part 3 - Classification 00:00:21
  • Logistic Regression 5 questions
  • Logistic Regression Intuition 00:17:06
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Logistic Regression in Python - Step 1 00:09:43
  • Logistic Regression in Python - Step 2 00:13:38
  • Logistic Regression in Python - Step 3 00:07:40
  • Logistic Regression in Python - Step 4 00:07:49
  • Logistic Regression in Python - Step 5 00:06:15
  • Logistic Regression in Python - Step 6 00:09:26
  • Logistic Regression in Python - Step 7 00:16:06
  • Logistic Regression in R - Step 1 00:05:58
  • Logistic Regression in R - Step 2 00:02:58
  • Logistic Regression in R - Step 3 00:05:23
  • Logistic Regression in R - Step 4 00:02:48
  • Warning - Update 00:00:27
  • Logistic Regression in R - Step 5 00:19:24
  • R Classification Template 00:04:16
  • Machine Learning Regression and Classification BONUS 00:00:17
  • BONUS: Logistic Regression Practical Case Study 00:00:16
  • K-Nearest Neighbor Intuition 00:04:52
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • K-NN in Python 00:19:58
  • K-NN in R 00:15:46
  • K-Nearest Neighbor 5 questions
  • SVM Intuition 00:09:49
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • SVM in Python 00:14:52
  • SVM in R 00:12:09
  • Kernel SVM Intuition 00:03:17
  • Mapping to a higher dimension 00:07:50
  • The Kernel Trick 00:12:20
  • Types of Kernel Functions 00:03:47
  • Non-Linear Kernel SVR (Advanced) 00:10:55
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Kernel SVM in Python 00:13:03
  • Kernel SVM in R 00:16:34
  • Bayes Theorem 00:20:25
  • Naive Bayes Intuition 00:14:03
  • Naive Bayes Intuition (Challenge Reveal) 00:06:04
  • Naive Bayes Intuition (Extras) 00:09:41
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Naive Bayes in Python 00:14:19
  • Naive Bayes in R 00:14:53
  • Decision Tree Classification Intuition 00:08:08
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Decision Tree Classification in Python 00:14:03
  • Decision Tree Classification in R 00:19:47
  • Random Forest Classification Intuition 00:04:28
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Random Forest Classification in Python 00:13:28
  • Random Forest Classification in R 00:19:56
  • Make sure you have this Model Selection folder ready 00:00:31
  • THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION! 00:21:00
  • False Positives & False Negatives 00:07:57
  • Confusion Matrix 00:04:57
  • Accuracy Paradox 00:02:12
  • CAP Curve 00:11:16
  • CAP Curve Analysis 00:06:19
  • Conclusion of Part 3 - Classification 00:02:09
  • Welcome to Part 4 - Clustering 00:00:21
  • K-Means Clustering Intuition 00:14:17
  • K-Means Random Initialization Trap 00:07:48
  • K-Means Selecting The Number Of Clusters 00:11:51
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • K-Means Clustering in Python - Step 1 00:08:25
  • K-Means Clustering in Python - Step 2 00:10:36
  • K-Means Clustering in Python - Step 3 00:16:58
  • K-Means Clustering in Python - Step 4 00:06:44
  • K-Means Clustering in Python - Step 5 00:19:35
  • K-Means Clustering in R 00:11:47
  • K-Means Clustering 5 questions
  • Hierarchical Clustering 5 questions
  • Hierarchical Clustering Intuition 00:08:47
  • Hierarchical Clustering How Dendrograms Work 00:08:47
  • Hierarchical Clustering Using Dendrograms 00:11:21
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Hierarchical Clustering in Python - Step 1 00:06:56
  • Hierarchical Clustering in Python - Step 2 00:17:12
  • Hierarchical Clustering in Python - Step 3 00:12:19
  • Hierarchical Clustering in R - Step 1 00:03:45
  • Hierarchical Clustering in R - Step 2 00:05:23
  • Hierarchical Clustering in R - Step 3 00:03:18
  • Hierarchical Clustering in R - Step 4 00:02:45
  • Hierarchical Clustering in R - Step 5 00:02:33
  • Conclusion of Part 4 - Clustering 00:00:12
  • Welcome to Part 5 - Association Rule Learning 00:00:11
  • Apriori Intuition 00:18:13
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Apriori in Python - Step 1 00:08:46
  • Apriori in Python - Step 2 00:17:07
  • Apriori in Python - Step 3 00:12:48
  • Apriori in Python - Step 4 00:19:41
  • Apriori in R - Step 1 00:19:53
  • Apriori in R - Step 2 00:14:24
  • Apriori in R - Step 3 00:19:17
  • Eclat Intuition 00:06:05
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Eclat in Python 00:12:00
  • Eclat in R 00:10:09
  • Welcome to Part 6 - Reinforcement Learning 00:00:35
  • The Multi-Armed Bandit Problem 00:15:36
  • Upper Confidence Bound (UCB) Intuition 00:14:53
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Upper Confidence Bound in Python - Step 1 00:12:42
  • Upper Confidence Bound in Python - Step 2 00:03:51
  • Upper Confidence Bound in Python - Step 3 00:07:16
  • Upper Confidence Bound in Python - Step 4 00:15:45
  • Upper Confidence Bound in Python - Step 5 00:06:12
  • Upper Confidence Bound in Python - Step 6 00:07:28
  • Upper Confidence Bound in Python - Step 7 00:08:09
  • Upper Confidence Bound in R - Step 1 00:13:39
  • Upper Confidence Bound in R - Step 2 00:15:58
  • Upper Confidence Bound in R - Step 3 00:17:37
  • Upper Confidence Bound in R - Step 4 00:03:18
  • Thompson Sampling Intuition 00:19:12
  • Algorithm Comparison: UCB vs Thompson Sampling 00:08:12
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Thompson Sampling in Python - Step 1 00:05:47
  • Thompson Sampling in Python - Step 2 00:12:19
  • Thompson Sampling in Python - Step 3 00:14:03
  • Thompson Sampling in Python - Step 4 00:07:45
  • Additional Resource for this Section 00:00:28
  • Thompson Sampling in R - Step 1 00:19:01
  • Thompson Sampling in R - Step 2 00:03:27
  • Welcome to Part 7 - Natural Language Processing 00:01:05
  • NLP Intuition 00:03:02
  • Types of Natural Language Processing 00:04:11
  • Classical vs Deep Learning Models 00:11:22
  • Bag-Of-Words Model 00:17:05
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Natural Language Processing in Python - Step 1 00:07:13
  • Natural Language Processing in Python - Step 2 00:06:45
  • Natural Language Processing in Python - Step 3 00:12:54
  • Natural Language Processing in Python - Step 4 00:11:00
  • Natural Language Processing in Python - Step 5 00:17:24
  • Natural Language Processing in Python - Step 6 00:09:52
  • Natural Language Processing in Python - BONUS 00:00:16
  • Homework Challenge 00:00:43
  • Natural Language Processing in R - Step 1 00:16:35
  • Natural Language Processing in R - Step 2 00:08:39
  • Natural Language Processing in R - Step 3 00:06:27
  • Natural Language Processing in R - Step 4 00:02:57
  • Natural Language Processing in R - Step 5 00:02:05
  • Natural Language Processing in R - Step 6 00:05:49
  • Natural Language Processing in R - Step 7 00:03:26
  • Natural Language Processing in R - Step 8 00:05:20
  • Natural Language Processing in R - Step 9 00:12:50
  • Natural Language Processing in R - Step 10 00:17:31
  • Homework Challenge 00:00:47
  • BONUS: NLP BERT 00:00:23
  • Welcome to Part 8 - Deep Learning 00:00:23
  • What is Deep Learning? 00:12:34
  • Plan of attack 00:02:51
  • The Neuron 00:16:24
  • The Activation Function 00:08:29
  • How do Neural Networks work? 00:12:47
  • How do Neural Networks learn? 00:12:58
  • Gradient Descent 00:10:12
  • Stochastic Gradient Descent 00:08:44
  • Backpropagation 00:05:21
  • Business Problem Description 00:04:59
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • ANN in Python - Step 1 00:10:21
  • Check out our free course on ANN for Regression 00:00:11
  • ANN in Python - Step 2 00:18:36
  • ANN in Python - Step 3 00:14:28
  • ANN in Python - Step 4 00:11:58
  • ANN in Python - Step 5 00:16:25
  • ANN in R - Step 1 00:17:17
  • ANN in R - Step 2 00:06:30
  • ANN in R - Step 3 00:12:29
  • ANN in R - Step 4 (Last step) 00:14:07
  • Deep Learning BONUS #1 00:00:24
  • BONUS: ANN Case Study 00:00:14
  • Plan of attack 00:03:31
  • What are convolutional neural networks? 00:15:49
  • Step 1 - Convolution Operation 00:16:38
  • Step 1(b) - ReLU Layer 00:06:41
  • Step 2 - Pooling 00:14:13
  • Step 3 - Flattening 00:01:52
  • Step 4 - Full Connection 00:19:24
  • Summary 00:04:19
  • Softmax & Cross-Entropy 00:18:20
  • Make sure you have your dataset ready 00:00:21
  • CNN in Python - Step 1 00:11:35
  • CNN in Python - Step 2 00:17:46
  • CNN in Python - Step 3 00:17:56
  • CNN in Python - Step 4 00:07:21
  • CNN in Python - Step 5 00:14:55
  • CNN in Python - FINAL DEMO! 00:23:38
  • Deep Learning BONUS #2 00:00:21
  • Welcome to Part 9 - Dimensionality Reduction 00:00:33
  • Principal Component Analysis (PCA) Intuition 00:03:49
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • PCA in Python - Step 1 00:16:52
  • PCA in Python - Step 2 00:05:30
  • PCA in R - Step 1 00:12:08
  • PCA in R - Step 2 00:11:22
  • PCA in R - Step 3 00:13:42
  • Linear Discriminant Analysis (LDA) Intuition 00:03:50
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • LDA in Python 00:14:52
  • LDA in R 00:19:59
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • Kernel PCA in Python 00:11:03
  • Kernel PCA in R 00:20:30
  • Welcome to Part 10 - Model Selection & Boosting 00:00:29
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • k-Fold Cross Validation in Python 00:17:55
  • Grid Search in Python 00:21:56
  • k-Fold Cross Validation in R 00:19:29
  • Grid Search in R 00:13:59
  • Make sure you have your Machine Learning A-Z folder ready 00:00:20
  • XGBoost in Python 00:14:48
  • Model Selection and Boosting BONUS 00:00:32
  • XGBoost in R 00:18:14
  • THANK YOU bonus video 00:02:40
  • ***YOUR SPECIAL BONUS*** 00:01:47
Requirements
  • Just some high school mathematics level.
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Description

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

  • Part 1 - Data Preprocessing

  • Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

  • Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

  • Part 4 - Clustering: K-Means, Hierarchical Clustering

  • Part 5 - Association Rule Learning: Apriori, Eclat

  • Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

  • Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP

  • Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

  • Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA

  • Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

Important updates (June 2020):

  • CODES ALL UP TO DATE

  • DEEP LEARNING CODED IN TENSORFLOW 2.0

  • TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!


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About the instructor
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Data Scientist
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